Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins.
Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins.
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DOI：
10.1186/1471-2105-11-s3-s3
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发表时间：
2010-04-29
影响因子：
3
通讯作者：
Zhang A
中科院分区：
文献类型：
--
作者：
Cho YR;Zhang A
蛋白质-蛋白质相互作用在细胞内蛋白质的生物过程中发挥着关键作用。最近的高通量技术已经生成了基因组规模的蛋白质-蛋白质相互作用数据。多种计算方法已应用于相互作用组网络分析，以揭示功能组织和途径。然而，由于复杂的连接性，它们受到了挑战。研究表明，蛋白质相互作用网络通常具有内在的拓扑特征：高度模块化和面向中心的结构。阐明模块和中枢的结构作用是复杂相互作用组网络分析的关键步骤。我们提出了一种新方法，将相互作用组网络的复杂结构转换为蛋白质的层次排序。该算法基于路径强度模型测量蛋白质之间的功能相似性，并揭示隐藏在复杂网络中的面向中心的树结构。我们对中心置信度进行评分，并识别通过我们的算法检索的蛋白质树结构中的功能模块。我们在酵母蛋白相互作用组网络中的实验结果表明，所选的中枢是执行功能所必需的蛋白质。在网络拓扑中，它们起到桥接不同功能模块的作用。此外，我们的方法在识别分层分布的功能模块方面具有很高的准确性。分解、转换和综合复杂的交互网络是对其结构行为进行建模的基本任务。在这项研究中，我们系统地分析了复杂的相互作用组网络结构以检索功能信息。与以前的层次聚类方法不同，该方法在全局视图中动态探索蛋白质的层次结构。由于其效率和可扩展性，它非常适用于高级生物体中的相互作用组网络。
Protein-protein interactions play a key role in biological processes of proteins within a cell. Recent high-throughput techniques have generated protein-protein interaction data in a genome-scale. A wide range of computational approaches have been applied to interactome network analysis for uncovering functional organizations and pathways. However, they have been challenged because ofcomplex connectivity. It has been investigated that protein interaction networks are typically characterized by intrinsic topological features: high modularity and hub-oriented structure. Elucidating the structural roles of modules and hubs is a critical step in complex interactome network analysis. We propose a novel approach to convert the complex structure of an interactome network into hierarchical ordering of proteins. This algorithm measures functional similarity between proteins based on the path strength model, and reveals a hub-oriented tree structure hidden in the complex network. We score hub confidence and identify functional modules in the tree structure of proteins, retrieved by our algorithm. Our experimental results in the yeast protein interactome network demonstrate that the selected hubs are essential proteins for performing functions. In network topology, they have a role in bridging different functional modules. Furthermore, our approach has high accuracy in identifying functional modules hierarchically distributed. Decomposing, converting, and synthesizing complex interaction networks are fundamental tasks for modeling their structural behaviors. In this study, we systematically analyzed complex interactome network structures for retrievingfunctional information. Unlike previous hierarchical clustering methods, this approach dynamically explores the hierarchical structure of proteins in a global view. It is well-applicable to the interactome networks in high-level organisms because of its efficiency and scalability.